Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry

Current machine learning approaches to landslide susceptibility modeling often involve grading conditioning factors, a method characterized by substantial subjectivity and randomness. The necessity and rationality of such grading have sparked continued debate. Recognizing the potential profound impact of this grading on the results of models, we conducted an in-depth study focusing on four townships within the Wanzhou section of the Three Gorges Reservoir area. A comprehensive assessment was conducted using three traditional machine learning models, five ensemble learning models, and four deep learning models to evaluate the implications of continuous factor grading. Three grading strategies were explored: non-grading, equal intervals, and natural breaks. Further investigation was conducted to determine how various grade levels (e.g., 4, 6, 8, 12, 16, 20) affect model efficacy. Our analysis reveals that the Support Vector Machine (SVM) model performs optimally with an 8-level grading using natural breaks. In contrast, a decision tree (DT) and its associated ensemble models are more effective without grading. For Multi-Layer Perceptron Neural Network (MLPNN) and Convolutional Neural Networks (CNN) models, a natural breaks grading exceeding 8 levels is advisable. Gated Recurrent Unit (GRU) and Deep Neural Networks (DNN) models benefit from an equidistant grading strategy of over 12 levels, while Long Short-Term Memory Neural Networks (LSTM) models thrive with an equidistant grading surpassing 16 levels. This study is pioneering in introducing grading guidelines for machine learning models in landslide susceptibility modeling. Our findings offer invaluable insights for future research, setting a path towards more standardized practices in this field. This enhances the bridge between theoretical knowledge and its real-world application, promoting a more rigorous and systematic grading approach and advancing the standardization of landslide susceptibility modeling.